Pruning-based oversampling technique with smoothed bootstrap resampling for imbalanced clinical dataset of Covid-19.

The Coronavirus Disease (COVID-19) was declared a pandemic disease by the World Health Organization (WHO), and it has not ended so far. Since the infection rate of the COVID-19 increases, the computational approach is needed to predict patients infected with COVID-19 in order to speed up the diagnosis time and minimize human error compared to conventional diagnoses. However, the number of negative data that is higher than positive data can result in a data imbalance situation that affects the classification performance, resulting in a bias in the model evaluation results.

Electronic nose dataset for pork adulteration in beef.

This article provides a dataset of several weight combinations from the adulteration of pork in beef using an electronic nose (e-nose). Seven combinations mixtures have been built, they were 100% pure beef, 10% mixed with pork, 25% mixed with pork, 50% mixed with pork, 75% mixed with pork, 90% mixed with pork, and 100% pure pork. By using this combination, a minimum of 10% of a mixture of pork or beef can be detected.

A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection.

A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy -nearest neighbours (Fuzzy -NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of 17 features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e.